Method of liveness detection and related device

Abstract

A method of liveness detection for a computing device comprises acquiring at least one full cycle of a remote photoplethysmography, rPPG, signal from a skin image, extracting at least one rPPG waveform characteristic from the full cycle of the rPPG signal, and determining whether the skin image includes a life according to the extracted rPPG waveform characteristic.

Description

BACKGROUND OF THE INVENTION1. Field of the Invention[0001]The present invention relates to a method of liveness detection, and more particularly, to a method and device for identifying an image as a living image or a fake image with remote photoplethysmography, rPPG.2. Description of the Prior Art[0002]Conventional liveness detection is realized by color textures, image reflection coefficients, and image depth information of the image, to prevent people from deceiving the face recognition system by a printed paper, photo, video, or 3D mask. However, this mechanism cannot effectively distinguish from high-quality or high-resolution images for avoiding deception. In addition, there are some mechanisms that require the user to be identified to make an active response such as blinking or mouth opening, which results in a poor user experience.[0003]There is also a mechanism for realizing liveness detection with physiological signals, which is mainly based on remote photoplethysmography, ...

AI Technical Summary

Benefits of technology

[0006]It is therefore an objective to provide a method of liveness detection and a related device, for improving the misjudgment of living organisms caused by light source changes and motion interference, to solve the above problem.

Problems solved by technology

However, this mechanism cannot effectively distinguish from high-quality or high-resolution images for avoiding deception.

In addition, there are some mechanisms that require the user to be identified to make an active response such as blinking or mouth opening, which results in a poor user experience.

However, the challenges of this technology include light source change and motion interference.

However, a fake image under a fixed frequency sway or a fixed frequency light source change may generate a cycle signal, so the physiological signal calculated according to the cycle signal, such as the heartbeat value, is easily controlled within the normal heartbeat range, which causes misjudgment of liveness in the skin image.

In addition, the time-frequency transform requires a large number of rPPG signals for calculation, so the rPPG spectrum will be stable after collecting a considerable number of signals, which cause that the subsequent face recognition system cannot be operated in time.

However, in the case of a fixed frequency sway or a fixed frequency light source change, a cycle signal is generated, such that the spectrum turbulence is also within the acceptable range, resulting in misjudgment of liveness.

Besides, an approach with cross-correlation performed on multiple rPPG signals obtained in different interest areas for wave similarity calculations between these areas still has the above problems.

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